AIMC Topic: Humans

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Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia.

PloS one
In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identi...

Integrated support vector machine and pharmacophore based virtual screening driven identification of thiophene carboxamide scaffold containing compound as potential PARP1 inhibitor.

Journal of biomolecular structure & dynamics
Poly (ADP-ribose) polymerase-1 (PARP1) inhibition strategy for cancer treatment is gaining advantage particularly in patients having a mutation in BRCA1/BRCA2 gene. To date, four drugs have obtained FDA approval and some inhibitors are in clinical tr...

Deep Learning for the Detection of Breast Cancers on Chest Computed Tomography.

Clinical breast cancer
BACKGROUND: Incidental breast cancers can be detected on chest computed tomography (CT) scans. With the use of deep learning, the sensitivity of incidental breast cancer detection on chest CT would improve. This study aimed to evaluate the performanc...

Improving phase-based conductivity reconstruction by means of deep learning-based denoising of phase data for 3T MRI.

Magnetic resonance in medicine
PURPOSE: To denoise phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system.

Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: This study aims to construct a strategy that uses assistance from artificial intelligence (AI) to assist radiologists in the identification of malignant versus benign focal liver lesions (FLLs) using contrast-enhanced ultrasound (...

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer.

BJU international
OBJECTIVE: To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors.

Decoding the microstructural properties of white matter using realistic models.

NeuroImage
Multi-echo gradient echo (ME-GRE) magnetic resonance signal evolution in white matter has a strong dependence on the orientation of myelinated axons with respect to the main static field. Although analytical solutions have been able to predict some o...

Robot-assisted versus open radical cystectomy: A cohort study on perioperative outcomes accounting for stage selection bias and surgical experience.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Most comparisons of robot-assisted (RARC) versus open radical cystectomy (ORC) for urothelial carcinoma do not factor the inherent stage selection bias or surgical experience.

Prediction of weaning from mechanical ventilation using Convolutional Neural Networks.

Artificial intelligence in medicine
Weaning from mechanical ventilation covers the process of liberating the patient from mechanical support and removing the associated endotracheal tube. The management of weaning from mechanical ventilation comprises a significant proportion of the ca...

A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans.

Computers in biology and medicine
Stress is the most prevailing and global psychological condition that inevitably disrupts the mood and behavior of individuals. Chronic stress may gravely affect the physical, mental, and social behavior of victims and consequently induce myriad crit...